Method and apparatus for assaying a drug candidate to estimate a pharmacokinetic parameter associated therewith

ABSTRACT

A method and apparatus for assaying a drug candidate with a biosensor having one or more sensing surface-bound biomolecules associated therewith are disclosed. The method comprises the steps of measuring the binding interaction between the drug candidate and the one or more sensing surface-bound biomolecules of the biosensor to obtain an estimate of at least one binding interaction parameter of the drug candidate, and then comparing the estimated binding interaction parameter against a mathematical expression correlated from binding interaction data associated with known drug compounds to determine an estimate of at least pharmacokinetic parameter of absorption, distribution, metabolism, or excretion (ADME) that is related to the drug candidate. The present invention allows for the simultaneous measurement of different pharmacokinetic parameters of the drug candidate, as well as an indication of the drug candidate&#39;s solubility, by use of a single analytical instrument. The pharmacokinetic data may be represented as a ADME characterization profile; such ADME profiles are of great utility for purposes of drug screening and lead optimization.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.09/921,496 filed Aug. 3, 2001, now allowed; which is a continuation ofU.S. patent application Ser. No. 09/336,865 filed Jun. 18, 1999,abandoned; both of which applications are incorporated herein byreference in their entireties.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention is generally directed to a method and apparatus forassaying a drug candidate and, more specifically, to a method formeasuring the binding interaction between a drug candidate and sensingsurface-bound biomolecules of a biosensor to determine a bindinginteraction parameter of the drug candidate, and then comparing thebinding interaction parameter against a predetermined drug correlationgraph (e.g., a mathematical expression) to estimate at least onepharmacokinetic parameter.

2. Description of the Related Art

A variety of experimental techniques are currently used to determinechemical, physical and biological properties associated with lowmolecular weight substances, particularly in the context of drugdiscovery. For example, researchers are often concerned with determininga variety of chemical, physical and biological properties associatedwith drug candidates for screening purposes. The determination of suchproperties often plays a pivotal role in the drug development andscreening process.

More specifically, it has long been recognized that the intensity andduration of the pharmacological effect of a systemically acting drug arefunctions not only of the intrinsic activity of the drug, but also ofits absorption, distribution, metabolism, and excretion (ADME)characteristics within the human body. These so-called ADMEcharacteristics are all intimately related to the scientific disciplineknown as “pharmacokinetics.” Pharmacokinetics is commonly referred to asthe study of the time courses (i.e., kinetics) associated with thedynamic processes of ADME of a drug and/or its metabolites within aliving organism, and is closely interrelated with the fields ofbiopharmaceutics, pharmacology, and therapeutics.

Because the body delays the transport of drug molecules acrossmembranes, dilutes them into various compartments of distribution,transforms them into metabolites, and eventually excretes them, it isoften difficult to accurately predict the pharmacological effect ofpromising new drug candidates. Researchers, however, commonly usepharmacokinetic ADME studies as one method to predict the efficacy of adrug at a site of action within the body.

Traditionally, researchers involved with preclinical ADME studies haveused pharmacokinetic/mathematical models coupled with actual drugconcentration data from blood (or serum or plasma) and/or urine, as wellas concentration data from various tissues, to characterize the behaviorand “fate” of a drug within living organisms. As is appreciated by thoseskilled in the art, the mathematical equations associated withpharmacokinetics are generally based on models that conceive the body asa multicompartmental organism. In such models it is presumed that thedrug and/or its metabolites are equitably dispersed in one or severalfluids/tissues of the organism. Any conglomerate of fluid or tissuewhich acts as if it is kinetically homogeneous may be termed a“compartment.” Each compartment acts as an isotropic fluid in which themolecules of drug that enter are homogeneously dispersed and wherekinetic dependencies of the dynamic pharmacokinetic processes may beformulated as functions of the amounts or concentrations of drug andmetabolites therein. Stated somewhat differently, the conceptualcompartments of the body are separated by barriers that prevent the freediffusion of drug among them; the barriers are kinetically definable inthat the rate of transport of drug or metabolite across membranebarriers between compartments is a function of, for example, the amountsor concentrations of drug and metabolites in the compartments, thepermeability of various membranes, and/or the amount of plasma proteinbinding and general tissue binding.

More specifically, pharmacokinetic/mathematical models are commonly usedby pharmacokineticists to represent drug absorption, distribution,metabolism, and excretion as functions of time within the varioustissues and organs of the body. In such models, the movement of theadministered drug throughout the body is concisely described inmathematical terms (e.g., a set of differential equations). Thepredictive capability of such models lies in the proper selection anddevelopment of mathematical functions that parameterize the essentialfactors governing the kinetic process under consideration.

For example, a drug that is administered by intravenous injection may beassumed to distribute rapidly in the bloodstream. Apharmacokinetic/mathematical model that describes this situation may bea tank containing a volume of fluid that is rapidly equilibrated withthe drug. Because a fraction of the drug in the body is continuallyeliminated as a function of time (e.g., excreted by the kidneys andmetabolized by the liver), the concentration of the drug in thehypothetical tank may be characterized by two parameters: (1) the volumeof fluid in the tank that will dilute the drug, and (2) the eliminationrate of the drug per unit of time, both of which are generallyconsidered to be constant. Thus, if a known set of drug concentrationsin the tank is determined at various time intervals by, for example,sampling, then the volume of fluid in the tank and rate of drugelimination may be estimated. This information may then, in turn, beused for predicting the disposition of the drug within a human body.

Theoretically, an unlimited number of models may be constructed todescribe the kinetic processes of drug absorption, distribution,metabolism, and excretion within the various tissues and organs of thehuman body. In general, however, the number of useful models is limiteddue to practical considerations associated with blood, tissue and/ororgan sampling. As a result, and as is appreciated by those skilled inthe art, two major types of models have been developed bypharmacokineticists: (1) compartmental models; and (2) physiologicmodels.

In pharmacokinetic compartmental models, the body is represented as aseries of compartments that communicate reversibly with each other. Eachcompartment is not a real physiological or anatomic region; rather, eachcompartment is considered to be inclusive of all tissues that havesimilar blood flow and drug affinity. For example, a compartmental modelmay consist of one or more peripheral compartments representingtissue(s) connected to a central compartment representing the bloodstream. Conceptually, the drug moves dynamically into and out of thecentral compartment and into and out of each of the peripheralcompartments. As such, rate constants may be used to represent theoverall rate process for the drug's disposition within each compartment.Compartment models are generally based on linear assumptions usinglinear differential equations, and are particularly useful when there islittle information known about the tissues and their respective drugconcentrations.

In contrast, pharmacokinetic physiologic models are based on knownanatomic and physiologic data, data which is kinetically described inview of the actual blood flow volumes responsible for distributing thedrug to the various parts of the body. Because there are many tissueorgans in the body, each tissue volume must be estimated and its drugconcentration and rate of change described mathematically (tissueshaving similar blood perfusion properties, however, are typicallygrouped together). Unfortunately, much of the information required toadequately describe such pharmacokinetic physiologic models are oftenvery difficult to obtain experimentally. Nevertheless, suchphysiologically based models are commonly used in conjunction withanimal data and interspecies scaling techniques to predict the drug'sdisposition within a human body.

More importantly, however, is that pharmacokinetic/mathematical models,and knowledge of their associated ADME parameters play an extremelyimportant role in drug discovery and development. A typical example is adrug that is active following intravenous administration but isconsiderably less active after comparable oral doses. Having appropriatepharmacokinetic information may reveal (1) whether the drug was poorlyabsorbed to yield subtherapeutic circulating levels, or (2) whether thedrug experienced presystemic metabolism to an inactive metabolite. Suchinformation may also provide guidance for subsequent decisions, such as(1) whether to improve drug absorption by altering the salt form orformulation, (2) whether to investigate the possibility of makingprodrugs, or (3) whether to consider a parenteral route ofadministration.

In addition to the foregoing, pharmacokinetic/mathematical models arealso generally considered extremely useful for, among other things: (1)predicting plasma, tissue, and urine drug levels with any dosageregimen; (2) calculating the optimum dosage regimen for an individualpatient; (3) estimating the possible accumulation of drugs and/ormetabolites; (4) correlating drug concentrations with pharmacologic andtoxicologic activity (i.e., pharmacodynamics); (5) evaluatingdifferences in the rate or extent of availability between formulations(i.e., bioequivalence); (6) describing how changes in physiology ordisease affect the absorption, distribution, and/or elimination of thedrug; and (7) explaining drug-drug and food-drug interactions.

Lastly, pharmacokinetic ADME data has also become an integral part ofthe pharmacological characterization process of promising new drugcandidates. Regulatory agencies, such as the U.S. Food and DrugAdministration, now require (1) a determination of pharmacokinetic ADMEdata in Phase I drug studies, and (2) a submission of pharmacokineticADME data as part of a New Drug Application. In this context, suchpharmacokinetic ADME data is deemed essential for predicting thebehavior and fate of the drug candidate within the human body.

Accordingly, there is a need in the art for improved methods fordetermining one or more pharmacokinetic parameters associated withabsorption, distribution, metabolism, and excretion of a drug candidate.There is also a need for apparatuses useful for carrying out suchmethods. The present invention fulfills these needs and provides furtherrelated advantages.

BRIEF SUMMARY OF THE INVENTION

In brief, the present invention is directed to a method and apparatusfor assaying a drug candidate. More specifically, this inventiondiscloses a method for measuring the binding interaction between a drugcandidate and sensing surface-bound biomolecules of a biosensor todetermine a binding interaction parameter of the drug candidate, andthen comparing the binding interaction parameter against a predetermineddrug correlation graph to estimate at least one pharmacokineticparameter of the drug candidate. The at least one pharmacokineticparameter may, for example, be one or more of ADME.

In another embodiment of the present invention, at least twopharmacokinetic parameters of the drug candidate are determined, and inyet another embodiment, at least one pharmacokinetic parameter and asolubility property of the drug candidate are determined. Suchpharmacokinetic parameters and/or solubility property may be determinedwhen the one or more sensing surface-bound biomolecules are selectedfrom, for example, liposomes, plasma proteins, CYP 450 enzymes,metabolic enzymes, or transport proteins.

The biosensor used in the practice of the present invention may utilizea mass-sensing technique, such as surface plasmon resonance. Inaddition, the biosensor may further employ a sensor chip, wherein thesensor chip comprises a free electron metal that includes a sensorsurface, wherein the free electron metal is copper, silver, aluminum orgold. The sensor chip may further comprise a hydrogel coupled to thesensor surface, wherein the hydrogel has a plurality of functionalgroups, and wherein the one or more sensing surface-bound biomoleculesare covalently bonded to the hydrogel.

In a more specific embodiment, a sensor surface adopted for use with abiosensor is disclosed. The sensor surface comprises a hydrogel matrixcoating coupled to a top surface of the sensor surface, wherein thehydrogel matrix coating has plurality of functional groups. At least twodifferent liposomes are bonded to the plurality of functional groups atdiscrete and noncontiguous locations on the hydrogel matrix coating ofthe sensor surface. In one embodiment, the sensor surface is a sensorchip, and a free electron metal is interposed between the hydrogelmatrix and the top surface of the sensor surface.

In another embodiment of this invention, an apparatus is disclosed forassaying a drug candidate, wherein the apparatus comprises a biosensorhaving one or more sensing surface-bound biomolecules associatedtherewith and capable of measuring at least one binding interactionparameter of the drug candidate, and a computer memory containing a datastructure for comparing the at least one binding interaction parameteragainst at least one mathematical expression correlated from bindinginteraction data associated with known drug compounds to determine anestimate of at least one pharmacokinetic parameter of the drugcandidate.

In yet a further embodiment, a computer memory containing a datastructure useful for assaying a drug candidate in accordance with themethods of the present invention is disclosed (as well as a generateddata signal conveying the same). The data structure may be used todetermine an estimate of at least pharmacokinetic parameter of the drugcandidate.

These and other aspects of the present invention will be evident uponreference to the following detailed description and related Figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates a sensorgram reflective of the steady-state bindinglevels associated with a selected drug-biomolecule interaction.

FIG. 1B illustrates an enlarged portion of the sensorgram of FIG. 1A andshows sensorgram irregularities.

FIG. 1C illustrates an enlarged portion of the sensorgram of FIG. 1A,wherein bulk-refractive index effects have been eliminated.

FIG. 2A illustrates a single “dip” depicting the reflected lightintensity associated with a homogeneous sensor surface.

FIG. 2B illustrates a number of “dips” depicting the non-averagedreflected light intensities associated with a non-homogeneous sensorsurface.

FIG. 2C illustrates a broadening of the “dip” depicting the averagedreflected light intensities associated with a non-homogeneous sensorsurface.

FIG. 3 shows a high-level block diagram of an exemplary computer systemfor assaying a drug candidate in accordance with the methods of thepresent invention.

FIG. 4 depicts a correlation graph having known KD values for knowncompounds plotted along the abscissa (i.e., the x-axis) andcorresponding measured KD values obtained via the biosensor plottedalong the ordinate (i.e., the y-axis) for nine drugs with known levelsof plasma protein binding

FIG. 5 depicts a correlation graph having respective binding levels at100 μM (R 100 μM) divided by the molecular weight of known drugcompounds' plotted along the abscissa (i.e., the x-axis) andcorresponding human serum albumin binding percentage, as measured byequilibrium dialysis, plotted along the ordinate (i.e., the y-axis).

FIG. 6 depicts reference subtracted sensorgram traces for each of threedrug candidates A-C.

FIG. 7 depicts a correlation graph having known fraction absorbed inhumans (FA %) plotted along the ordinate (i.e., the y-axis) andcorresponding calibrated (i.e., reference subtracted) steady statebinding levels for each drug at 500 μM plotted along the abscissa in a10-logarithm scale (i.e., the x-axis).

DETAILED DESCRIPTION OF THE INVENTION

The present invention is directed to a method for assaying a drugcandidate and, more specifically, to a method and apparatus formeasuring the binding interaction between at least one drug candidateand sensing surface-bound biomolecules of a biosensor to determine abinding interaction parameter of the drug candidate, and then comparingthe binding interaction parameter against a predetermined drugcorrelation graph (e.g., a mathematical expression fitted to a series ofknown data points) to determine an estimate of at least onepharmacokinetic parameter. Although many specific details of certainembodiments of the invention are set forth in the following detaileddescription and accompanying Figures, those skilled in the art willrecognize that the present invention may have additional embodiments, orthat the invention may be practiced without several of the detailsdescribed herein.

For purposes of clarity and to assist in understanding the full scope ofthe present invention, a brief review of the nomenclature associatedwith pharmacokinetics has been provided. As used within the context ofthe present invention, the following pharmacokinetic terms shall beconstrued broadly, and shall have their generally accepted meanings asset forth below. (As previously noted, “pharmacokinetics” refers to thestudy of the kinetics associated with the dynamic processes ofabsorption, distribution, metabolism, and excretion (ADME) of a drugand/or its metabolites within a living organism.)

“Absorption” refers to the process of uptake of a drug compound from thesite of administration into the systemic circulation. The transfer ofdrug across the intestinal lumen is generally referred to as oralabsorption, whereas the transfer of drug across an externalphysiological barrier is referred to general absorption. As disclosedherein, the pharmacokinetic parameter of absorption may be estimatedfrom biosensor data associated with a sensor chip having, for example, aplurality of appropriate liposomes immobilized thereon.

“Distribution” refers to the transfer of a drug compound from the siteof administration to the total systemic circulation and then toextracellular and intracellular water and tissues. Drug distribution isusually a rapid and reversible process. As disclosed herein, thepharmacokinetic parameter of distribution may be estimated frombiosensor data associated with a sensor chip having, for example, aplurality of appropriate plasma proteins, liposomes, and/or transportproteins immobilized thereon.

“Metabolism” refers to the sum of all the chemical reactions forbiotransformation of endogenous and exogenous substances which takeplace in the living cell. As disclosed herein, the pharmacokineticparameter of metabolism may be estimated from biosensor data associatedwith a sensor chip having, for example, a plurality of appropriatemetabolic enzymes immobilized thereon.

“Excretion” refers to the final elimination or loss of a drug from thebody. Drug excretion includes both passive diffusion and relativespecific carrier mediated excretion. Drugs may be excreted, unchanged oras metabolites, in urine via the kidneys or in feces via the bile and/orthe intestine. Volatile compounds are often excreted in expired air bythe lungs. As disclosed herein, the pharmacokinetic parameter ofexcretion may be estimated from biosensor data associated with a sensorchip having immobilized thereon, for example, an antibody thatspecifically detects the drug, as well as other proteins/receptorshaving a high affinity/specificity against the drug candidate. Suchantibodies and proteins/receptors may be used to quantify theconcentration/amount of the drug in different body fluids (e.g.,urine/feces) and tissues, using a direct binding assay.

In addition to these ADME parameters, the solubility of a drug is alsoan important property that may be measured by the methods of the presentinvention. “Solubility” refers to the ability of two substances to forma homogeneous solution or mixture with each other. Solubility isimportant for the dissolution of drug given in solid dosage form. Asdisclosed herein, the solubility of a drug candidate may be estimatedfrom sensorgram irregularities associated with reflectance minimum anddip-shape of biosensor data.

Furthermore, and as used herein, the term “parameter” refers to aconstant or variable term in a function (e.g., a mathematicalexpression) that determines the specific form of the function but notnecessarily its general nature. For example, the constant term “a” inthe functionf(x)=ax, where “a” determines only the slope of the linedescribed by f(x), is referred to as a parameter. As such, the term“binding interaction parameter” refers to those constant or variableterms that are related to the binding interaction between a drugcandidate and a sensing surface-bound biomolecule and includes, forexample, association and dissociation rate constants, as well as maximumbinding capacity. Similarly, the term “pharmacokinetic parameter” refersto those constant and variable terms that are related to the dispositionof the drug candidate within a living organism and includes, forexample: volume of distribution; total clearance; protein binding;tissue binding; metabolic clearance; renal clearance; hepatic clearance;biliary clearance; intestinal absorption; bioavailability; relativebioavailability; intrinsic clearance; mean residence time; maximum rateof metabolism; Michaelis-Menten constant; partitioning coefficientsbetween tissues and blood (or plasma) such as those partitioningcoefficients associated with the blood brain barrier, blood placentabarrier, blood human milk partitioning, blood adipose tissuepartitioning, and blood muscle partitioning; fraction excreted unchangedin urine; fraction of drug systemically converted to metabolites;elimination rate constant; half-life; and secretion clearance.

The methods of the present invention are intended to be carried out byuse of an affinity-based biosensor. As is appreciated by those skilledin the art, “biosensors” are analytical devices for analyzing minutequantities of sample solution having an analyte of interest, wherein theanalyte is analyzed by a detection device that may employ a variety ofdetection methods. Typically, such methods include, but are not limitedto, mass detection methods, such as piezoelectric, optical,thermo-optical and surface acoustic wave (SAW) device methods, andelectrochemical methods, such as potentiometric, conductometric,amperometric and capacitance methods. With regard to optical detectionmethods, representative methods include those that detect mass surfaceconcentration, such as reflection-optical methods, including bothinternal and external reflection methods, angle, wavelength or phaseresolved, for example, ellipsometry and evanescent wave spectroscopy(EWS), the latter including surface plasmon resonance (SPR)spectroscopy, Brewster angle refractometry, critical anglerefractometry, frustrated total reflection (FTR), evanescent waveellipsometry, scattered total internal reflection (STIR), optical waveguide sensors, evanescent wave-based imaging such as critical angleresolved imaging, Brewster angle resolved imaging, SPR angle resolvedimaging, and the like. Further, photometric methods based on, forexample, evanescent fluorescence (TIRF) and phosphorescence may also beemployed, as well as waveguide interferometers.

In the detailed description and Examples that follow, the presentinvention is illustrated in the context of SPR spectroscopy. However, itis to be understood that the present invention is not limited to thisdetection method. Rather, any affinity-based detection method where ananalyte binds to a ligand immobilized on a sensing surface may beemployed, provided that a change in a property of the sensing surface ismeasured and quantitatively indicative of binding of the analyte to theimmobilized ligand thereon. In the context of SPR spectroscopy, oneexemplary class of biosensors is sold by Biacore AB (Uppsala, Sweden)under the tradename BIACORE® (hereinafter referred to as “the BIACOREinstrument”). Such biosensors utilize a SPR based mass-sensing techniqueto provide a “real-time” binding interaction analysis between a surfacebound ligand and an analyte of interest.

The BIACORE instrument includes a light emitting diode, a sensor chipcovered with a thin gold film, an integrated fluid cartridge and a photodetector. Incoming light from the diode is reflected in the gold filmand detected by the photo detector. At a certain angle of incidence(“the SPR angle”), a surface plasmon wave is set up in the gold layer,which is detected as an intensity loss or “dip” in the reflected light.More particularly, and as is appreciated by those skilled in the art,the phenomenon of surface plasmon resonance (SPR) associated with theBIACORE instrument is dependent on the resonant coupling of light,incident on a thin metal film, to oscillations of the conductingelectrons, called plasmons, at the metal film surface. Theseoscillations give rise to an evanescent field that extends from thesurface into the sample solution. When resonance occurs, the reflectedlight intensity drops at a sharply defined angle of incidence, the SPRangle, which is dependent on the refractive index within the reach ofthe evanescent field in the proximity of the metal surface.

Stated somewhat differently, surface plasmon resonance is an opticalphenomenon arising in connection with total internal reflection of lightat a metal film-liquid interface. Normally, light traveling through anoptically denser medium, e.g., a glass prism, is totally reflected backinto the prism when reaching an interface of an optically less densemedium, e.g., a buffer, provided that the angle of incidence is largerthan the critical angle. This is known as total internal reflection.Although the light is totally reflected, a component of the incidentlight momentum called the evanescent wave penetrates a distance of theorder of one wavelength into the buffer. The evanescent wave may be usedto excite molecules close to the interface. If the light ismonochromatic and p-polarized, and the interface between the media iscoated with a thin (a fraction of the light wave-length) metal film, theevanescent wave under certain conditions will interact with freeoscillating electrons (plasmons) in the metal film surface. When surfaceplasmon resonance occurs, light energy is lost to the metal film and thereflected light intensity is thus decreased.

The resonance phenomenon will only occur for light incident at a sharplydefined angle which, when all else is kept constant, is dependent on therefractive index in the flowing buffer close to the surface. Changes inthe refractive index out to about 1 μm from the metal film surface canthus be followed by continuous monitoring of the resonance angle. Adetection volume is defined by the size of the illuminated area at theinterface and the penetration depth of the evanescent field. It shouldbe noted that no light passes through the detection volume (the opticaldevice on one side of the metal film detects changes in the refractiveindex in the medium on the other side).

As noted above, the SPR angle depends on the refractive index of themedium close to the gold layer. In the BIACORE instrument, dextran istypically coupled to the gold surface, with the ligand being bound tothe surface of the dextran layer. (Note a detailed discussion of matrixcoatings for biosensor sensing surfaces is provided in U.S. Pat. No.5,436,161, which is incorporated herein by reference in its entirety.)The analyte of interest is injected in solution form onto the sensorsurface through the fluid cartridge. Because the refractive index in theproximity of the gold film depends upon (1) the refractive index of thesolution (which is constant) and, (2) the amount of material bound tothe surface, the binding interaction between the bound ligand andanalyte can be monitored as a function of the change in SPR angle.

A typical output from the BIACORE instrument is a “sensorgram,” which isa plot of response (measured in “resonance units” or “RU”) as a functionof time. An increase of 1,000 RU corresponds to an increase of mass onthe sensor surface of approximately 1 ng/mm². As a sample containing theanalyte contacts the sensor surface, the ligand bound to the sensorsurface interacts with the analyte in a step referred to as“association.” This step is indicated on the sensorgram by an increasein RU as the sample is initially brought into contact with the sensorsurface. Conversely, “dissociation” normally occurs when sample flow isreplaced by, for example, a buffer flow. This step is indicted on thesensorgram by a drop in RU over time as analyte dissociates from thesurface-bound ligand. A detailed discussion of the technical aspects ofthe BIACORE instrument and the phenomenon of SPR may be found in U.S.Pat. No. 5,313,264, which is incorporated herein by reference in itsentirety.

In addition, a detailed discussion of the technical aspects of thebiosensor sensor chips used in connection with the BIACORE instrumentmay be found in U.S. Pat. No. 5,492,840, which is incorporated herein byreference in its entirety. This patent discloses, among other things,that each sensor chip may have a plurality of sensing surfaces, and thatsuch sensing surfaces may be arranged in series or parallel with respectto the fluid sample pathway of the fluid cartridge. This patent alsodiscloses that each of the plurality of sensing surfaces of a singlesensor chip may have bound thereto a unique type of ligand that iscapable of interacting with an analyte in its own characteristic way.

For example, and as disclosed herein, each of the four discrete sensingsurfaces of the BIACORE instrument may have immobilized thereonbiomolecules such as liposomes, plasma proteins, CYP 450 enzymes, othermetabolic enzymes, and/or transport/efflux proteins. By immobilizing oneor a selected combination of at least two different liposomes, plasmaproteins, CYP 450 enzymes, other metabolic enzymes, and/ortransport/efflux proteins, onto the one or more discrete sensingsurfaces of a sensor chip, one or more pharmacokinetic parametersassociated with a drug candidate may be readily determined. Morespecifically, and in one embodiment of the present invention, it hasbeen discovered that (1) an estimate of an “absorption” parameter of adrug candidate may be determined from the binding interactions betweenthe drug candidate and one or more appropriate liposomes; (2) anestimate of a “distribution” parameter of a drug candidate may bedetermined from the binding interactions between the drug candidate andan appropriate set of plasma proteins (e.g., specific and nonspecifictissue binding and tissue permeability); (3) an estimate of a“metabolism” parameter of a drug candidate may be determined from thebinding interactions between the drug candidate and an appropriate setof metabolic enzymes; and (4) an estimate of an “excretion” parameter ofa drug candidate may be determined from the binding interactions betweenthe drug candidate and an appropriate set of transport proteins.

Within the context of the present invention, suitable liposomes forestimating an “absorption” parameter of a drug candidate include organiccompounds originating from natural or synthetic lipid molecules such asglycerophospholipids, glyceroglycolipids, sphingophospholipids andsphingoglycolipids, and from the classes phosphatidyl choline,phosphatidyl etanolamine, phosphatidyl serine, phosphatidyl glycerol,phosphatidyl acid, phosphatidyl inositol, galactopyranoside,digalactopyranoside, ceramide-phosphatidyl choline,ceramide-phosphatidyl etanolamine, ceramide-phosphatidyl serine,ceramide-phosphatidyl glycerol, ceramide-phosphatidyl acid,ceramide-phosphatidyl inositol, sphingomyelin molecules,glucosylceramides, glucocerebrosides, galactoceramides,galactocerebrosides, gangliosides, monoacyl phosphatidyl choline,cardiolipin molecules, that may be linked to saturated or unsaturatedfatty or fluorocarbon chains ranging from eight to twenty-four carbonsin length where fatty chains attached to the head group can be the sameor of different structure, cholesterol, lanosterol, ergosterol,stigmasterol, sitosterol and derivatives thereof capable of beingincorporated into lipid membranes,N,N-dimethyl-N-octadecyl-1-octadecanammonium chloride or bromide,(N-[1-(2,3-dioleoyloxy)propyl]-N,N,N-trimethylammonium chloride,diacetyl phosphate,N-[2,3-dihexadecyloxy)prop-1-yl]-N,N,N-trimethylammonium chloride,bolaamphiphiles, polyglycerolmonoalkylethers, polyethoxymonoalkylethers,as well as liposome-forming molecules from the classes amphiphilicpolymers, amino acids, crown ether compounds anddi(acyloxy)dialkylsilanes. The liposomes of the present invention andthe like may be immobilized onto the sensing surface by the techniquedisclosed in Example 1.

Suitable plasma proteins for estimating a “distribution” parameter of adrug candidate include proteins such as Immunoglobulin G(7s-y-globulin), IgG; Immunoglobulin A, IgA; Secretory IgA, s IgA;Immunoglobulin M (19s-y-globulin) IgM; Immunoglobulin D, IgD;Immunoglobulin E, IgE; α1-Antitrypsin, α1 Pl, α1 A; α1-Antichymotrypsin,(α1X-Glycoprotein) α1 X; Inter-oc-Trypsin inhibitor, 1αTI; AntithrombinIII, (heparin cofactor) AT III; α-Thiol Proteinase inhibitor (LMWkininogen) αTPI; C1-Inactivator, C1 esterase inhibitor(α2-neuraminoglycoprotein) C1 INA: α2-Macroglobulin, α2 M;α2-Antiplasmin, α2AP; Cystatin C (Post-y-globulin; y-Trace protein); C1q(11S protein); C1r; C1s; C2; C3 (β1C-globulin), C4 (β1E-globulin); C5(β1F-globulin); C6; C7; C8; C9; Factor B, (C3-proactivator;β2-glycoprotein II; glycine-rich β-glycoprotein); Factor D(C3-proactivator convertase); Properdin, P; Factor I, (C3b inactivator);C4-binding Protein; Fibrinogen, Fl, Prothrombin, F II; Factor V(proaccelerin); FV; Factor VII, (proconvertin), F VII; Factor VIII: C(antihemophilic factor) F VIII: C; Factor VIII—Related Antigen; FV III:Rag (von Willebrand factor) (VWF); Factor IX (Christmas factor) F IX;Factor X, (Stuart-Power factor) F X; Factor XI (plasma thromboplalstinantecedent) F XI; Factor XII (Hageman factor) F XII; Factor XIII (fibrinstabilizing factor) F XIII; high-molecular-weight (HMW) Kininogen(Fitzgerald factor); Prekallikrein (Fletcher factor); Plasminogen;Protein C; Protein S; Allbumin, ALB; Haptoglobin, HP, Hp 1-1, Hp 2-1, Hp2-2; Prealbumin (transthyretin, thyroxine-binding prealbumin);Retinol-binding Protein RBP; Thyroxine-binding Globulin TBG; Transcortin(corticosteroid-binding globulin) CBG; Sex hormone-binding globulin(Steroid-binding β Globulin) SHBG; Vitamin D-binding Protein(Gc-globulin, group specific component) VDBP; Transcobalamin I, TC I;Trancobalamin II, TC II; Transferrin (siderophilin) TF; Ferritin;Hemopexin, HPX; Apolipoprotein A, Apo A-1, Apo A-II; Apolipoprotein B,Apo B-48, Apo B-100; Apolipoprotein C; Apo C-I, Apo C-II, Apo C-III;Apolipoprotein E, Apo E; Apolipoprotein (a), apo (a); Serum Amyloid A,SAA; α-Fetoprotein, AFP; α1-Acid Glycoprotein (orosomucoid) α1 AG;Ceruloplasmin, CP; Serum Amyloid P protein (9.5S α1-glycoprotein;α1-macroglobulin) SAP; α2-HS Glycoprotein, α2 HS; Fibronectin (coldinsoluble globulin) FN, C-reactive Protein, CRP; β2-microglobulin, β2 M;Pregnancy-specific β1-glycoprotein, SPI; α1-microglobulin, α1 M. Theplasma proteins of the present invention and the like may be immobilizedonto the sensing surface through the use of known immobilizationtechniques as is appreciated by those skilled in the art.

Suitable metabolic enzymes for estimating a “metabolism” parameterand/or an “excretion” parameter of a drug candidate include cytochromeP450-enzymes selected from the class of cytochrome P450 enzymes havingan active site characterized by a protoporphyrin IX-iron complex withthiolate from a cysteine of the enzyme serving as the fifth ligand toiron. Suitable CYP 450 enzymes include CYTOCHROME P450, CYP1A1, CYP1A2,CYP2A1, 2A2, 2A3, 2A4, 2A5, 2A6, CYP2B1, 2B2, 2B3, 2B4, 2B5, 2B6,CYP2C1, 2C2, 2C3, 2C4, 2C5, 2C6, 2C7, 2C8, 2C9, 2C10, 2C11, 2C12,CYP2D1, 2D2, 2D3, 2D4, 2D5, 2D6, CYP2E1, CYP3A1, 3A2, 3A3, 3A4, 3A5,3A7, CYP4A1, 4A2, 4A3, 4A4, CYP4 μl 1, CYP P450 (TXAS), CYP P450 11A(P450scc), CYP P450 17(P45017a), CYP P450 19 (P450arom), CYP P450 51(P45014a), CYP P450 105A1, CYP P450 105B1. Other important metabolicenzymes include Glutathione-thioethers, LeukotrieneC4,butyrylcholinesterase, human serum paraoxonase/arylesterase,N-Acetyltransferase, UDP-glucuronosyltransferase (UDPGT) isoenzymes, TLPST, TS PST, drug glucosidation conjugation enzyme, theglutathione-S-transferases (GSTs) (RX:glutathione-R-transferase), GST1,GST2, GST3, GST4, GST5, GST6, alcohol dehydrogenase (ADH), ADH I, ADHII, ADH III, aldehyde dehydrogenase (ALDH), cytosolic (ALDH1),mitochondrial (ALDH2), monoamine oxidase, MAO: Ec 1.4.3.4, MAOA, MAOB,flavin-containing monoamine oxidase, enzyme superoxide dismutase (SOD),Catalase, amidases, N1,-monoglutathionyl spermidine,N1,N8-bis(glutathionyl) spermidine, Thioesters, GS-SG, GS-S-cysteine,GS-S-cysteinylglycine, GS-S-O3H, GS-S-CoA, GS-S-proteins, S-carbonicanhydrase III, S-actin, Mercaptides, GS-Cu(I), GS-Cu(II)-SG, GS-SeH,GS-Se-SG, GS-Zn-R, GS-Cr-R, Cholin esterase, lysosomal carboxypeptidase,Calpains, Retinol dehydrogenase, Retinyl reductase, acyl-CoA retinolacyltrunderase, folate hydrolases, protein phosphates (pp) 4 st, PP-1,PP-2A, PP-2 Bpp-2C, deamidase, carboxyesterase, Endopeptidases,Enterokinase, Neutral endopeptidase E.C.3.4.24.1 1, Neutralendopeptidase, carboxypeptidases, dipeptidyl carboxypeptidase, alsocalled peptidyl-dipeptidase A or angiotensin-converting enzyme (ACE)E.C.3.4.15.1, carboxypeptidase M, g-Glutamyl transpeptidase E.C.2.3.2.2,Carboxypeptidase P, Folate conjugase E.C.3.4.12.10, Dipeptidases,Glutathione dipeptidase, Membrane Gly-Leu peptidases, Zinc-stableAsp-leu dipeptidase, Enterocytic intracellular peptidases, Aminotripeptidase E.C.3.4.11.4, Amino dipeptidase E.C.3.4.13.2,Prodipeptidase, Arg-selective endoproteinase; the family of brush borderhydrolases, Endopeptidase-24.11, Endopeptidase-2(meprin), Dipeptidylpeptidase IV, Membrane dipeptidase GPI, Glycosidases,Sucrase-isomaltase, Lactase-glycosyl-ceraminidase, Glucoamylase-maltase,Trehalase, Carbohydrase enzymes, alfa-Amylase (pancreatic),Disaccharidases (general), Lactase-phhlorizin hydrolase, Mammaliancarbohydrases, Glucoamylase, Sucrase-Isomaltase, Lactase-glycosylceramidase, Enzymatic sources of ROM, Xanthine oxidase, NADPH oxidase,Amine oxidases, Aldehyde oxidase, Dihydroorotate dehydrogenase,Peroxidases, Human pancreatic exocrine enzymes, Trypsinogen 1,Trypsinogen 2, Trypsinogen 3, Chymotrypsinogen, proElastase 1,proElastase 2, Protcase E,Kallikreinogen, proCarboxypeptidase A1,proCarboxypeptidase A2, proCarboxypeptidase B1, proCarboxypeptidase B2,Glycosidase, Amylase, lipases, Triglycaride lipase, Collipase, Carboxylester hydrolase, Phospholipase A2, Nucleases, Dnase I, Ribonucleotidereductase (RNRs), Wistar rat exocrine pancreatic proteins, Label ProteinIEP, A1 Amylase 1, A2 Amylase 2, Lipase, CEL Carboxyl-ester lipase, PLProphospholipase A, Ti Trypsinogen 1, T2 Trypsinogen 2, T3 Trypsinogen3, T4 Trypsinogen 4, C1 Chymotrypsinogen 1, C2 Chymotrypsinogen 2, PE1Proelastase 1, PE2 Proelastase 2, PCA Procarboxypeptidase A1, PCA1Procarboxypeptidase A2, PCB1 Procarboxypeptidase B1, PCB2Procarboxypeptidase B2, R Ribonuclease, LS Lithostatin, Characteristicsof UDPGT isoenzymes purified from rat liver, 4-nitrophenol UDPGT,17b-Hydroxysteriod UDDPGT, 3-a-Hydroxysteroid UDPGT, Morphine UDPGT,Billirubin UDPGT, Billirubin monoglucuronide, Phenol UDPGT,5-Hydroxytryptamine UDPGT, Digitoxigenin monodigitoxide UDPGT,4-Hydroxybiphenyl UDPGT, Oestrone UDPGT, Peptidases, Aminopeptidase N,Aminopeptidase A, Aminopeptidase P, Dipeptidyl peptidase IV,b-Casomorphin, Angiotensin-converting enzyme, Carboxypeptidase PAngiotensin II, Endopeptidase-24.11, Endopeptidase-24.18 Angiotensin I,Substance P (deamidated), Exopeptidase,1. NH2 terminus Aminopeptidase N(EC 3.4.11.2), Aminopeptidase A (EC 3.4.11.7), Aminopeptidase P (EC3.4.11.9), Aminopeptidase W (EC 3.4.11.-), Dipeptidyl peptidase IV (EC3.4.14.5), g-Glutamyl transpeptidase (EC 2.3.2.2), 2. COOH terminusAnglotensin-converting enzyme (EC 3.4.15.1), Carboxypeptidase P (EC3.4.17.-), Carboxypeptidase M (EC 3.4.17.12),3. Dipeptidase Microsomaldipeptidase (EC 3.4.13.19), Gly-Leu peptidase, Zinc stablepeptidase,Endopeptidase Endopeptidase-24.11 (EC 3.4.24.11),Endopeptidase-2 (EC 3.4.24.18, PABA-peptide hydrolase, Meprin,Endopeptidase-3, Endopeptidase (EC 3.4.21.9), GST A1-1, Alpha,GST A2-2Alpha, GST M1a-1a Mu, GST M1b-1b Mu, GST M2-2 Mu, GST M3-3 Mu, GST M4-4Mu, GST M5-5 Mu, GST P1-1 Pi, GST T1-1 Theta, GST T2-2 Theta, MicrosomalLeukotriene C4 synthase, UGT isozymes, UGT1.1, UGT1.6, UGT1.7, UGT2.4,UGT2.7, UGT2.11, Pancreatic enzymes, Elastase, Aminopeptidase(dipeptidyl aminopeptidase (IV), Chymotrypsin, Trypsin, CarboxypeptidaseA, Methyltransferases, O-methyltransferases, N-methyltransferases,S-methyltransferases, Catechol-O-methyltransferases,MN-methyltransferase, S-sulphotransferases, Mg²⁺-ATPase, Growth factorreceptors Alkaline phosphatase, ATPases, Na, K⁺ ATPase, Ca²⁺-ATPase,Leucine aminopeptidase, K⁺ channel. The metabolic enzymes of the presentinvention and the like may be immobilized onto the sensing surfacethrough the use of known immobilization techniques as is appreciated bythose skilled in the art.

Suitable transport proteins for estimating an “excretion” (as well as anefflux, absorption, distribution) parameter of a drug candidate includeGlucose a. Na⁺-glucose cotransp GLUT 1, b. Facilitative transporter,GLUT 1-5, GLUT-2, Neutral amino acid transporter, Na⁺-independent systemL amino acid transporter, cationic amino acid, Y⁺ cationic L-amino acidtransporter, Dipeptides H⁺ contransport, Nucleosides Na⁺-dependent andfacilitative, Taurine Na⁺ and Cl⁻ dependent, Bile acids Na⁺/bile acidcotransporter, Na⁺-independent bile acid transporter, ABC transporters,Prostaglandins facilitative transpoprter, Na⁺/H⁺ exchanger Antiporter,Phosphate Na⁺/Pi cotransporter, Sulfate Na⁺-cotransporter, Transportersfor neurotransmitters, Norepinephrine Na⁺/Cl⁻ cotransporter, DopamineNa⁺/Cl⁻ cotransporter, Serotonine Na⁺/Cl⁻ cotransporter, GABA, GAT-1,Na⁺/Cl⁻ dependent, Glycine Na⁺/Cl⁻ dependent, Glutamate Na⁺cotransporter, K⁺/OH⁻ counter-transport, ABC transporters,P-glycoprotein (MDR1, MDR 20R MDR 3), Cl⁻ channel (CFTR), Antigenicpeptides, TAP1 and TAP2 heterodimer, Lung resistance protein (LRP),Multidrug resistance protein 1 (MRP1), Multidrug resistance protein 2(MRP2 or cMOAT), Multidrug resistance protein 3 (MRP3), Multidrugresistance protein 4 (MRP4), Multidrug resistance protein 5 (MRP5),Multidrug resistance protein 6 (MRP6), mrp (mouse), EBCR (rabbit), C.elegans mrp1 (nematode), C. elegans mrp2 (nematode), MRP6 (human), YCF1(yeast), AtMRP1 (Arabidopsis), SUR1 (human), sur2 (rat, mouse),YOR1/YRS1 (yeast), LtpgpA (leishmania), Hepatic amino acid transportsystem, Neutral amino acid transporters, MeAIB, Dicarboxylic aminoacids, Neutral amino acids (branched), b-amino acids, Long chain fattyacids, Monoglycerides, L-Lysophosphatidylcholine, Transport proteins forbilirubin, Bilitranslocase (BTL), Organic anion binding protein (OABP),BSP/bilirubin binding protein, Signal receptor andtransduction-hydrolases, ATPases, Na⁺ dependent/independent bile acidtransport, Bilirubin/BSP carrier (Cl-dependent), SO/OH exchangerCl⁻channel Na⁺/H⁺ exchanger, Na⁺/HCO³ cotransport, GSH-, GSSH-, GSconjugate carrier, SO⁴/HCO³ exchanger, Na⁺-dependent amino acidtransport, Dipolar amino acid transporter, Basic amino acids, Cystine,Imino acids Cl⁻, b-Amino acids Cl⁻, XAG Acidic amino acids K⁺, A Dipolara-amino acids, three-and four-carbon dipolar amino acids, L Bulky,hydrophobic, dipolar amino acids, y+ Basic amino acids, folatetransperter, cbl transport proteins, Na⁺—K⁻—ATPase, Bile acidtransperter (BAT), protein kinase C, Na⁺/I⁻ sympoter (NIS); Bile saltexport pump(BSEP, cBAT, SPGP); Intestinal bile acid transporters; Purineselective Na⁺-dependent nucleoside transporter (hSPNTI); Pyrimidineselective Na⁺-dependent nucleoside transporter (cNTI); Mitoxantronetransporter (MXR1 and MXR2); Intestinal oligopeptide transporter(PepT1);Renal oligopeptide transporter(PepT2); Breast cancer resistance protein(BCRP). The transport/efflux proteins of the present invention and thelike may be immobilized onto the sensing surface through the use ofknown immobilization techniques as is appreciated by those skilled inthe art.

As stated above, it has been discovered that the pharmacokineticparameters of absorption, distribution, metabolism, and excretion (ADME)of a drug candidate may be determined from the binding interactionsbetween the drug candidate and appropriate sensing surface-boundbiomolecules (e.g., from different liposomes, plasma proteins, CYP 450enzymes, other metabolic enzymes, and/or transport/efflux proteins, suchas those as identified above) of a biosensor. That is, the bindinginteractions between a drug candidate and one or more sensingsurface-bound biomolecules selected from liposomes, plasma proteins, CYP450 enzymes, other metabolic enzymes, and transport/efflux proteins, maybe measured via the BIACORE instrument to determine a “bindinginteraction parameter” of the drug candidate. The estimated bindinginteraction parameter may then, in turn, be compared against apredetermined drug correlation graph to estimate one or more absorption,distribution, metabolism, and excretion “pharmacokinetic parameters” ofthe drug candidate. Moreover, the pharmacokinetic parameter of the drugcandidate may be, for example, selected from the group consisting ofvolume of distribution; total clearance; protein binding; tissuebinding; metabolic clearance; renal clearance; hepatic clearance;biliary clearance; intestinal absorption; bioavailability; relativebioavailability; intrinsic clearance; mean residence time; maximum rateof metabolism; Michaelis-Menten constant; partitioning coefficientsbetween tissues and blood (or plasma) such as those partitioningcoefficients associated with the blood brain barrier, blood placentabarrier, blood human milk partitioning, blood adipose tissuepartitioning, and blood muscle partitioning; fraction excreted unchangedin urine; fraction of drug systemically converted to metabolites;elimination rate constant; half-life; and secretion clearance; any oneof which may be determined from appropriate sensorgrams of the selecteddrug-biomolecule interaction (alternatively, they may be determined byextracting score vectors with principal component analysis fromdigitalized interaction profiles, as well as other known multivariatemethods).

For example, to estimate an apparent equilibrium constant between a drugcandidate and selected sensing surface-bound biomolecules, the followingprocedure may be employed. First, a concentration series (e.g., 0, 20,50, 100, 500, and 1,000 μM) of the drug candidate may be prepared, andsequentially injected into a biosensor having a sensor chip operativelyassociated therewith, wherein the sensor chip has a reference sensingsurface and at least one sensing surface with surface-boundbiomolecules. The relative responses at steady-state binding levels foreach drug concentration level may then be measured. Because ofbulk-refractive index contributions from solvent additives in thebiosensor's running buffer, a correction factor may be calculated (viaknown calibration procedures) and applied to give corrected relativeresponses. The corrected relative responses for each drug concentrationmay then be mathematically evaluated as is appreciated by those skilledin the art to estimate the apparent equilibrium constant.

More specifically, the apparent equilibrium constant (KD) between thedrug candidate and the selected sensing surface-bound biomolecules maybe calculated by fitting the measured equilibrium (R_(eq)) data andknown drug concentration (C) data to Equation (1):R _(eq) =C*R _(max)/(C+KD)+offset  (1)wherein R_(max) is the maximum binding capacity of the sensing surface.Alternatively, the apparent equilibrium constant (KD) may be calculatedfrom as little as two concentrations (i.e., C1 and C2) of the drugcandidate by use of Equation (2):KD=(R _(eqC1) /C1−R _(eqC2) /C2)/(R _(eqC2) −R _(eqC1))  (2)The estimated apparent equilibrium constant (KD) or binding level at aspecific molar drug concentration may then, in turn, be compared againsta predetermined drug correlation graph to estimate one or moreabsorption, distribution, metabolism, and excretion parameters of thedrug candidate.

In the context of the present invention, a predetermined drugcorrelation graph refers to a mathematical expression or function thathas been developed from binding interaction data associated with knowndrug compounds. For example, a correlation graph may be constructedwherein known binding interaction parameters (e.g., apparent equilibriumconstants and/or binding levels at specific molar drug concentrations)for known compounds are plotted along the abscissa (i.e., the x-axis)and corresponding measured binding interaction parameters obtained viathe biosensor are plotted along the ordinate (i.e., the y-axis), or viceversa. Stated somewhat differently, the correlation plot is a graphicalrepresentation of a mathematical expression that correlates known andmeasured affinity data.

By comparing the estimated binding interaction parameter obtained fromthe selected drug-biomolecule interaction against the mathematicalexpression (i.e., correlation graph) correlated from known and measuredaffinity data, it has been surprisingly discovered that an estimate of apharmacokinetic ADME parameter related to the drug candidate may beaccurately predicted. In addition, by immobilizing a selectedcombination of at least two different liposomes, plasma proteins, CYP450 enzymes, other metabolic enzymes, and/or transport/efflux proteins,onto one or more discrete sensing surfaces of a sensor chip, it has beenfurther surprisingly discovered that at least two pharmacokineticparameters associated with a drug candidate may be readily determined.Moreover, by immobilizing a selected combination of different liposomes,plasma proteins, CYP 450 enzymes, other metabolic enzymes, and/ortransport/efflux proteins, onto the sensing surfaces of biosensor, suchas in a predetermined line of different spots of biomolecules, an ADMEpattern or profile, such as a drug candidate characterization matrix,may be readily developed. Such ADME profiles are of great utility forpurposes of drug screening.

In addition to determining one or more pharmacokinetic parameters bymonitoring the refractive index changes of a biosensor as disclosedabove, the solubility of a drug candidate may also be simultaneouslydetermined (together with the one or more pharmacokinetic parameters) bymonitoring the minimum, maximum or centroid of the drug-biomoleculeinteraction signal (as is disclosed, for example, in PCT Publication No.WO 97/09618, which is incorporated herein by reference in its entirety).More specifically, the solubility of the drug candidate may beestimated/identified from irregularities, as well as from reflectanceminimum (R_(min)) and dip-shape data, shown in the sensorgram of thedrug-biomolecule interaction. In general, the concentration at whichprecipitation occurs is referred to as the solubility limit; thisproperty may be important to measure because it may indicate that thedrug candidate has an affinity greater than otherwise indicated.

Solubility problems of the drug candidate may be detected becauseinsoluble particles (i.e., precipitates) tend to bind to sensingsurface-bound biomolecules, thereby causing the sensing surface to benon-homogenous. (A homogenous surface has the same surface concentrationthroughout, whereas a non-homogenous surface has concentrationdisruptions.) A non-homogenous sensing surface actually measures severalrefractive indices, which are all averaged together in the biosensor'sdetector. These multiple measurements from a single, but non-homogenoussensing surface, tend to result in an increase in reflectance minimumand a broadening of the dip associated therewith.

An illustration of how sensorgram irregularities may be used to identifysolubility problems (i.e., the presence of precipitates) is shown inFIGS. 1A-C. More specifically, the steady-state binding levelsassociated with a selected drug-biomolecule interaction is generallyshown in FIG. 1A. By enlarging or “zooming” in on the top area of thesensorgram, which is reflective of the steady-state binding, thesensorgram irregularities become more apparent as is shown incorresponding FIG. 1B. The sensorgram irregularities become even moreapparent by using reference subtracted data (i.e., bulk-refractive indexeffects have been eliminated) as is shown in corresponding FIG. 1C.

An illustration of how reflectance minimum (R_(min)) and dip-shape datamay be used to identify solubility problems (i.e., the presence ofprecipitates) is shown in FIGS. 2A-C. More specifically, a homogeneoussurface has the same surface concentration throughout, and will thusresult in a single “dip” with respect to the reflected light intensityas shown in FIG. 2A. When precipitates bind to the sensing surface, thesensing surface becomes non-homogenous which tends to result in anincrease in reflectance minimum and a broadening of the dip associatedtherewith as is shown in FIGS. 2B and 2C, respectively.

Based on the foregoing methods for assaying a drug candidate,researchers may now simultaneously measure several differentpharmacokinetic parameters of the drug candidate, as well as gauge thedrug candidate's solubility, by using a single analytical instrument.The present invention simplifies and improves the rate of drug discoveryand development because important pharmacokinetic data may now bereadily obtained at a relatively early stage of the process.

In addition to the foregoing methods, the present invention is alsodirected to apparatuses adapted to carrying out such methods. Morespecifically, the apparatuses of the present invention comprise abiosensor having a sensing surface associated therewith, and a computersystem that facilitates the implementation of the steps associated withthe methods disclosed herein. The computer includes a computer memorycontaining a data structure useful for assaying a drug candidate; thedata structure comprises binding interaction data associated with knowndrug compounds such that the data structure may be used to determine anestimate of at least pharmacokinetic parameter of the drug candidate.

The aspect of the present invention relating to a computer memorycontaining a data structure useful for assaying a drug candidate may bemore fully illustrated in the context of a high-level computer blockdiagram as is depicted in FIG. 3. As shown, such a computer system 300contains one or more central processing units (CPUs) 310, input/outputdevices 320, and the computer memory containing a data structure usefulfor assaying a drug candidate(memory) 330. Among the input/outputdevices is a storage device 321, such as a hard disk drive, and acomputer-readable media drive 322, which may be used to install softwareproducts, where the software products are provided on acomputer-readable medium, such as a CD-ROM. The input/output devicesalso include a network connection 323, through which the computer system300 may communicate with other connected computer systems, such asnetworks. The input/output devices may also contain a display 324 and adata input device 325.

The memory 330 preferably contains an operating system 331, such asMICROSOFT WINDOWS, for providing to other programs access to resourcesof the computer system. The memory 330 preferably further containssoftware 332. While the computer memory containing a data structureuseful for assaying a drug candidate is preferably implemented on acomputer system configured as described above, those skilled in the artwill recognize that it may also be implemented on computer systemshaving different configurations.

In a related aspect, the present invention is also directed to agenerated data signal conveying a data structure useful for assaying adrug candidate. As above, the data structure comprises bindinginteraction data associated with known drug compounds, such that thedata structure may be used to determine an estimate of at leastpharmacokinetic parameter of the drug candidate.

In still a further embodiment, a sensor surface adapted for use with abiosensor is disclosed. The sensor surface has a hydrogel matrix coatingcoupled to the top surface of the sensor surface, wherein the hydrogelmatrix coating has a plurality of functional groups, and at least twodifferent types of liposomes are bonded to the plurality of functionalgroups at discrete and noncontiguous locations on the hydrogel matrixcoating of the sensor surface (such as disclosed in Example 1). In thecontext of the BIACORE instrument as described above, the sensor surfaceis preferably in the form a sensor chip, wherein the sensor chip has afree electron metal interposed between the hydrogel matrix and the topsurface of the sensor chip. Suitable free electron metals in this regardinclude copper, silver, aluminum and gold.

The sensor surface may have a lipophilic substance interposed betweenthe different types of liposomes and the plurality of functional groups,wherein the lipophilic substance is covalently bonded to the pluralityof functional groups. Representative lipophilic substances comprise analkyl chain having from 12 to 24 carbon atoms, such as stearylamine.Similarly, representative liposomes include1,2-dimyristol-sn-glycero-3-phosphocholine (DMPC) and1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC). The sensorsurfaces of this invention may also have non-liposome biomoleculesassociated therewith. For example, human serum albumin, CYP 450 enzyme,a metabolic enzyme, and/or a transport protein may be bonded to theplurality of may be bonded to the plurality of functional groups atdiscrete and noncontiguous locations on the hydrogel matrix coating ofthe sensor surface.

For purposes of illustration and not limitation, the following examplesmore specifically disclose various aspects of the present invention.

EXAMPLES Example 1 Simultaneous Measurement of Solubility, PlasmaProtein Binding Lipophilicity and Intestinal Absorption for Three DrugCandidates A-C

This example discloses how combined information from each of fourdifferent flow-cells of a biosensor may be represented in acharacterization matrix where each of the three drug candidates A-Cillustrates a quality pattern (i.e., HSA % Bound, PredictedLipophilicity, Solubility, and Predicted FA %) which is useful for theselection of lead drug compounds.

Preparation of Sensor Chip

Three of the four discrete sensing surfaces of a CM5 Sensor Chip(Biacore AB, Uppsala, Sweden) were modified such that the CM5 SensorChip had surface-bound biomolecules as depicted below in Table 1. TABLE1 SURFACE-BOUND BIOMOLECULES OF CM5 SENSOR CHIP Type of Surface/Cell No.Flow-Cell Surface Modification FC1 Ref-1 Unmodified carboxymethyldextran (CM5) FC2 Target-1 Human Serum albumin - HSA (9-12 kRU) FC3Target-2 DMPC-liposomes (5-7 kRU) captured on stearylamine FC4 Target-3POPC-liposomes (5-7 kRU) captured on stearylamine

More specifically, and to achieve covalent attachment of stearylamine tosurface/cell nos. 3 and 4, the following procedure was employed. A CM5Sensor Chip was first inserted into a BIACORE 3000 biosensor (BiacoreAB, Uppsala, Sweden). The flow of running buffer (isotonic phosphatebuffer pH 7.0 (9.6 g Na₂HPO₄.2H₂O, 1.7 g KH₂PO₄, 4.1 g NaCl to 1 liter))with 2% dimethylsulfoxide (DMSO) was directed to flow-cells 3 and 4 byusing appropriate software commands. A mixture of 0.2 MN-ethyl-N′-(3-diethylaminopropyl)-carbodiimide and 50 mMN-hydroxysuccinimide in water was then injected into the biosensor so asto flow over flow-cells 3 and 4 for a period of 10 minutes. The CM5Sensor Chip was then washed with running buffer, removed from thebiosensor, and placed in a petri dish on top of a few layers of tissuepaper that had been moistened with ethanol. The exposed sensing surfaceswere then treated with 30 μl of a 10 mM stearylamine solution in 99%ethanol. After another 45 minute period, the CM5 sensor chip was gentlywashed with ethanol and then with water. Next, the exposed sensingsurfaces were further treated with 50 μl of a 1M ethanolamine solutionat pH 8.5. Finally, and after another 10 minute period, the sensingsurfaces were washed with HBS-buffer.

Following covalent attachment of stearylamine to surface/cell nos. 3 and4- and after the CM5 Sensor Chip that had just been washed withHBS-buffer was blown dry with nitrogen and reinserted into thebiosensor—human serum albumin (HSA), DMPC-liposomes, and POPC-liposomes(available from Sigma) were then captured onto surface/cell nos. 2, 3and 4, respectively. The following procedures were employed to capturethese biomolecules.

To capture human serum albumin the flow was initially directed toflow-cell 2 by using appropriate software commands. The sensing surfaceof flow-cell 2 was then activated for a period of 7 minutes withEDC/NHS. Next, human serum albumin was injected at 15 μg/ml in 10 mMacetate buffer pH 5.2 for a period of 7 minutes; then 1M ethanolamine pH8.5 was injected for another period of 7 minutes. This procedureresulted in the immobilization of 9-12 kRU of human serum albumin to thesensing surface of surface/cell no. 2.

To capture the two different liposomes the flow was first directed toflow-cell 3, and 0.5-1 mM DMPC liposome was injected until 5-7 kRU ofthe DMPC liposome had been captured. The flow was then directed toflow-cell 4, and 0.5-1 mM POPC liposome was injected until 5-7 kRU ofthe POPC liposome had been captured. Finally, the flow-system of thebiosensor was washed with 100 mM NaOH, and the autoinjector tubing waswashed with 0.5% SDS and 50 nM glycine pH 9.5 to remove trace lipids.

Calibration Procedure

In order to improve data quality by reducing bulk-refractive indexcontributions from the DMSO solvent additive, a calibration procedurewas employed. (Note the calibration procedure required varyingconcentrations of DMSO.) Because the running buffer contained 2% DMSO, aseries of 8 to 10 calibration solutions were made that had varying DMSOconcentrations ranging from 1.5% to 3.0%. Each calibration solution wassequentially injected over each of the four sensing surfaces by usingthe serial injection mode of the biosensor, and the respectivesteady-state binding levels were measured. The relative responses of thereference sensing surface of flow-cell 1 (FC1 having unmodified CM5 as areference surface) were then subtracted from the correspondingcalibration responses of the target sensing surfaces of flow-cells 2, 3,and 4, respectively, and plotted as functions of the relative responses.From these plots, calibration functions were calculated for each of therespective sensing surfaces.

The calibration functions were then used to calculate appropriatecorrection factors for the samples containing the three drug candidatesA-C. That is, for the samples containing the drug candidates, therelative responses of the steady state binding levels of the referencesensing surface (i.e., signals from flow-cell 1) were measured and therespective calibration functions were used to calculate correctionfactors appropriate for each drug candidate. The correction factors werethen applied (i.e., subtracted) to the differences between the referenceresponses and the sample responses to give a responses where thebulk-refractive index contributions from the DMSO solvent additive hadbeen eliminated.

Corrected Relative Responses of Drug Candidates A-C

The modified CM5 Sensor Chip (having surface-bound biomolecules asdepicted above in Table 1) was used to measure the binding interactionof the three drug candidates A-C. Concentration series (i.e., 0, 20, 50,100, 500, and 1,000, μM) of each drug candidate were prepared, andsequentially injected in serial mode into the biosensor such that foursensorgrams corresponding to each flow-cell were developed. Correctedrelative responses at steady-state binding levels for each drugconcentration were determined for each of the target sensingsurfaces/flow-cells; such data is presented below in Tables 2-4. TABLE 2DRUG CONCENTRATION DATA FOR HUMAN SERUM ALBUMIN Candidate 10 μM 50 μM100 μM 500 μM 1,000 μM A 50 110 130 140 145 B 0 0 0 10 50 C 10 50 100110 115

TABLE 3 DRUG CONCENTRATION DATA FOR POPC LIPOSOME Candidate 10 μM 50 μM100 μM 500 μM 1,000 μM A 10 300 400 450 500 B 0 0 40 60 70 C 0 0 0 0 10

TABLE 4 DRUG CONCENTRATION DATA FOR DMPC LIPOSOME Candidate 10 μM 50 μM100 μM 500 μM 1,000 μM A 0 50 70 90 95 B 0 10 20 50 55 C 0 0 0 0 0Analysis of Data

The corrected relative responses for each drug concentration as depictedabove in Tables 2-4 were, among other things, adjusted for differencesin molecular weight of the drug candidates, as well as transformed toincrease correlation with other properties. For example, for each targetsensing surface/flow-cell, an apparent equilibrium constant (KD) wascalculated by fitting the measured equilibrium (R_(eq)) data and knowndrug concentration (C) data to Equation (3):R _(eq) =C*R _(max)/(C+KD)+offset  (3)wherein R_(max) is the maximum binding capacity of the sensing surface.In the fitting procedure, R_(max), KD, and the offset were calculated.

Alternatively, the apparent equilibrium constant (KD) may have beencalculated from two concentrations of the drug candidate, C1 and C2, byuse of Equation (4):KD=(R _(eqC1) /C1−R _(eqC2) /C2)/(R _(eqC2) −R _(eqC1))  (4)

In either case, the KD values obtained via the biosensor weresubsequently correlated with known KD values calculated from reportedhuman serum albumin binding percentages (i.e., reported bindingpercentages that have been reported for known drug compounds), therebyenabling a prediction of the degree of protein binding for each of thethree drug candidates A-C. In other words, a correlation graph as shownin FIG. 4 was constructed having known KD values for known compoundsplotted along the abscissa (i.e., the x-axis) and corresponding measuredKD values obtained via the biosensor plotted along the ordinate (i.e.,the y-axis). This correlation graph was then used to predict the degreeof plasma protein binding for each of the three drug candidates A-C (seebelow).

In addition, for each target sensing surface/flow-cell, a molecularweight adjusted response at a single concentration level was used as athreshold value for ranking the drug candidates A-C. More specifically,a correlation graph as shown in FIG. 5 was constructed, whereinrespective binding levels at 100 μM (R 100 μM) divided by the known drugcompounds' molecular weight were plotted along the abscissa (i.e., thex-axis) and corresponding human serum albumin binding percentage, asmeasured by equilibrium dialysis, were plotted along the ordinate (i.e.,the y-axis). This correlation graph was then used to discriminatebetween strong and weak human serum albumin binders for each of thethree drug candidates A-C.

Furthermore, for each target sensing surface/flow-cell, referencesubtracted sensorgram traces (e.g., FC2 minus FC1) were developed todetect the presence of precipitates (caused by low solubility). Morespecifically, reference subtracted sensorgram traces for each of thethree drug candidates A-C, as shown in FIG. 6, were constructed. (Notethat when precipitates form and then bind/associate with the sensingsurface, the response signal will decrease in a non-continuous way.) Byanalyzing the concentration series for each of the three drug candidatesA-C (as shown in FIG. 6), it was determined that drug candidate Aprecipitated and thus had relatively low solubility, whereas drugcandidates B-C did not precipitate and thus had relatively highsolubilities.

Finally, and as shown below in Table 5, the combined information fromeach target sensing surface/flow cell was tabulated into a reducedcharacterization matrix where each drug candidate A-C received a qualitypattern that was useful for selection of the lead drug compounds. (Notethat the predicted lipophilicity and fraction absorbed are prophetic.)TABLE 5 CHARACTERIZATION MATRIX FOR DRUG CANDIDATES A-C Pred. CandidateHSA % Bound Lipophilicity Solubility Predicted FA % A 99.9 4 <1 μM >97 B<90 2 OK >90 C >90 −2 OK <50

Based on the forgoing characterization matrix, it was determined thatdrug candidate B was the preferred compound due to its low plasmaprotein binding level (HSA % bound <90), medium lipophilicity (predictedlipophilicity=2), no identified solubility problems (solubility=OK), andacceptable intestinal absorption (predicted fraction absorbed >90).

Example 2 Demonstrated Correlation Between Biosensor Data and FractionAbsorbed in Humans (FA %)

This example discloses a correlation between biosensor data obtainedform the BIACORE instrument (i.e., BIACORE 3000) and known data for thefraction absorbed in humans (FA %) for a number of different drugs,wherein the correlation graph is useful for drug candidate absorptionpredictions. More specifically, a correlation graph as shown in FIG. 7was constructed having known fraction absorbed in humans (FA %) plottedalong the ordinate (i.e., the y-axis) and corresponding calibrated(i.e., reference subtracted) steady state binding levels for each drugat 500 μM plotted along the abscissa in a 10-logarithm scale (i.e., thex-axis). In this example, the sensing surfaces of the target flow-cellseach had 6,000 RU of POPC-GM3 ganglioside (available from Sigma)captured on stearylamine tiles. (Note that an unmodified sensing surfaceof a CM5 Sensor Chip was used as the reference.) As shown in FIG. 7,there is high degree of correlation between the biosensor data and theknown fraction absorbed in humans (FA %) data, as is evidenced by themathematical expression/function that has been fitted to the variousdata points.

Moreover, the correlation graph also shows a classification of thevarious substances, wherein absorption of the substances using thepassively transported trans-cellular route through the intestine aredepicted by h=high, m=medium, and 1=low, and wherein absorption usingactive transport is depicted by h-act=high, and wherein absorption ofsubstances with molecular weights <200 using the para-cellular route isdepicted by h-para=high and m-para=medium, respectively. (Note theSulfasalazine is a pre-drug which rapidly decomposes in the intestine,which may explain its outlier properties; Piroxicam has a very lowsolubility <<<<500 μM, which may explain its lack of correlation.)

While the present invention has been described in the context of theembodiments illustrated and described herein, the invention may beembodied in other specific ways or in other specific forms withoutdeparting from its spirit or essential characteristics. Therefore, thedescribed embodiments are to be considered in all respects asillustrative and not restrictive. The scope of the invention is,therefore, indicated by the appended claims rather than by the foregoingdescription, and all changes that come within the meaning and range ofequivalency of the claims are to be embraced within their scope.

All of the above U.S. patents, U.S. patent application publications,U.S. patent applications, foreign patents, foreign patent applicationsand non-patent publications referred to in this specification and/orlisted in the Application Data Sheet, are incorporated herein byreference, in their entirety.

1-19. (Cancelled)
 20. A computer memory containing a data structureuseful for assaying a drug candidate by measuring the bindinginteraction between the drug candidate and the one or more sensingsurface-bound biomolecules of the biosensor to obtain at least onebinding interaction parameter of the drug candidate, and comparing theat least one binding interaction parameter against at least onemathematical expression correlated from binding interaction dataassociated with known drug compounds to determine an estimate of atleast one pharmacokinetic parameter of the drug candidate, the datastructure comprising binding interaction data associated with known drugcompounds such that the data structure may be used to determine anestimate of at least one pharmacokinetic parameter of the drugcandidate.
 21. A generated data signal for conveying a data structureuseful for assaying a drug candidate by measuring the bindinginteraction between the drug candidate and the one or more sensingsurface-bound biomolecules of the biosensor to obtain at least onebinding interaction parameter of the drug candidate, and comparing theat least one binding interaction parameter against at least onemathematical expression correlated from binding interaction dataassociated with known drug compounds to determine an estimate of atleast one pharmacokinetic parameter of the drug candidate, the datastructure comprising binding interaction data associated with known drugcompounds such that the data structure may be used to determine anestimate of at least one pharmacokinetic parameter of the drugcandidate.
 22. An apparatus for assaying a drug candidate, the apparatuscomprising a biosensor having one or more sensing surface-boundbiomolecules associated therewith and capable of measuring at least onebinding interaction parameter of the drug candidate, and a computermemory containing a data structure for comparing the at least onebinding interaction parameter against at least one mathematicalexpression correlated from binding interaction data associated withknown drug compounds to determine an estimate of at least onepharmacokinetic parameter of the drug candidate.
 23. The apparatus ofclaim 22 wherein the at least one pharmacokinetic parameter is anabsorption parameter, a distribution parameter, a metabolism parameter,or an excretion parameter.
 24. The apparatus of claim 22 wherein the atleast one pharmacokinetic parameter is volume of distribution, totalclearance, protein binding, tissue binding, metabolic clearance, renalclearance, hepatic clearance, biliary clearance, intestinal absorption,bioavailability, relative bioavailability, intrinsic clearance, meanresidence time, maximum rate of metabolism, Michaelis-Menten constant,partitioning coefficients between tissues and blood or plasma, fractionexcreted unchanged in urine, fraction of drug systemically converted tometabolites, elimination rate constant, half-life, or secretionclearance.
 25. The apparatus of claim 24 wherein the partitioningcoefficients between tissues and blood or plasma are partitioningcoefficients associated with the blood brain barrier, blood placentabarrier, blood human milk partitioning, blood adipose tissuepartitioning, or blood muscle partitioning.
 26. The apparatus of claim22 wherein an estimate of at least two pharmacokinetic parameters of thedrug candidate are determined.
 27. The apparatus of claim 22 wherein thebiosensor utilizes a mass-sensing technique.
 28. The apparatus of claim27 wherein the mass-sensing technique involves surface plasmonresonance.
 29. The apparatus of claim 28 wherein the at least onemathematical expression correlated from binding interaction dataassociated with known drug compounds is a function fitted to a pluralityof data points plotted on a Cartesian coordinate system.
 30. Theapparatus of claim 22 wherein the plurality of sensing surface-boundbiomolecules are selected from liposomes, plasma proteins, CYP 450enzymes, metabolic enzymes, or transport proteins.
 31. The apparatus ofclaim 22 wherein the biosensor utilizes a sensor chip comprising: ahydrogel coupled to the sensor surface, wherein the hydrogel has aplurality of functional groups, and wherein the one or more sensingsurface-bound biomolecules are bonded to the hydrogel.
 32. The apparatusof claim 31 wherein the sensor chip further comprises: a free electronmetal that includes a sensor surface, wherein the free electron metal isselected from the group consisting of copper, silver, aluminum and gold.33. The apparatus of claim 32 wherein the biosensor is capable ofdetecting surface plasmon resonance associated with the free electronmetal.
 34. The apparatus of claim 32 wherein the hydrogel is apolysaccharide or a water-swellable organic polymer.
 35. The apparatusclaim 34 wherein the polysaccharide is dextran.
 36. The apparatus ofclaim 31 wherein the plurality of functional groups of the hydrogel ofthe sensor chip include one or more of a hydroxyl, carboxyl, amino,aldehyde, carbonyl, epoxy or vinyl functional group.
 37. The apparatusof claim 31 wherein a signal associated with a reflected light beam withrespect to time is detected, and wherein the reflected light beamestablishes a surface plasmon resonance with the free electron metal.38. The apparatus of claim 37 wherein the signal associated with thereflected light beam defines a resonance curve of the surface plasmonresonance.
 39. The apparatus of claim 37 wherein the signal associatedwith the reflected light beam defines a reflectance minimum of thesurface plasmon resonance.
 40. A sensor surface adapted for use with abiosensor, comprising: a hydrogel matrix coating coupled to a topsurface of the sensor surface, wherein the hydrogel matrix coating has aplurality of functional groups; and at least two different types ofliposomes bonded to the plurality of functional groups, wherein the atleast two different types of liposomes are at discrete and noncontiguouslocations on the hydrogel matrix coating of the sensor surface.
 41. Thesensor surface of claim 40 wherein a free electron metal is interposedbetween the hydrogel matrix and the top surface of the sensor surface,wherein the free electron metal is selected from the group consisting ofcopper, silver, aluminum and gold.
 42. The sensor surface of claim 40,further comprising at least one lipophilic substance interposed betweenthe at least two different types of liposomes and the plurality offunctional groups, wherein the lipophilic substance is covalently bondedto the plurality of functional groups.
 43. The sensor surface of claim42 wherein the lipophilic substance comprises an alkyl chain having from12 to 24 carbon atoms.
 44. The sensor surface of claim 42 wherein thelipophilic substance is stearylamine.
 45. The sensor surface of claim 40wherein the at least two different liposomes are1,2-dimyristol-sn-glycero-3-phosphocholine (DMPC) and1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC).
 46. The sensorsurface of claim 40, further comprising human serum albumin bonded tothe plurality of functional groups at discrete and noncontiguouslocations on the hydrogel matrix coating of the sensor surface.
 47. Thesensor surface of claim 40, further comprising one or more of a CYP 450enzyme, a metabolic enzyme, or transport protein bonded to the pluralityof functional groups at discrete and noncontiguous locations on thehydrogel matrix coating of the sensor surface.